{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<font face=\"XB Zar\" size=5><div dir=rtl align=center>\n",
        "<font face=\"IranNastaliq\" size=5>\n",
        "به نام خدا\n",
        "</font>\n",
        "<br>\n",
        "<font size=3>\n",
        "دانشگاه صنعتی شریف - دانشکده مهندسی کامپیوتر\n",
        "</font>\n",
        "<br>\n",
        "<font color=blue size=5>\n",
        "مقدمه‌ای بر یادگیری ماشین\n",
        "</font>\n",
        "<br>\n",
        "<hr/>\n",
        "<font color=red size=6>\n",
        "فصل شش\n",
        "<br>\n",
        "CNN Architecture \n",
        "</font>\n",
        "<br>\n",
        "نویسندگان:‌ آرین امانی\n",
        "<hr>\n",
        "\n",
        "<br>\n",
        "  <div align=\"right\">\n",
        "  <font color=\"red\" size=5>فهرست مطالب</font>\n",
        "\t<ul>\n",
        "    <li>\n",
        "\t\t  <a href=\"#Kernels\">\n",
        "        آشنایی با لایه کانولوشنی\n",
        "        <ul>\n",
        "          <li>\n",
        "            <a href=\"#recap\">\n",
        "              یاد آوری شبکه کاملا متصل(Fully Connected)\n",
        "            </a>\n",
        "          </li>\n",
        "          <li>\n",
        "            <a href=\"#cnn\">\n",
        "              شبکه پیچشی(Convolutional)\n",
        "            </a>\n",
        "          </li>\n",
        "        </ul>\n",
        "    </a>\n",
        "\t\t</li>\n",
        "</div></font>"
      ],
      "metadata": {
        "id": "h1OkOqv428jR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.utils import to_categorical, plot_model\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "jLw_7U7RxmBc"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "  <div dir=rtl id=\"dataset\">\n",
        "  <font face=\"XB Zar\" size=4>\n",
        "      <font color=\"red\" size=5>\n",
        "      آشنایی با لایه کانولوشنی\n",
        "      </font>\n",
        "      <hr/>\n",
        "      یاد آوری شبکه پیچشی کاملا متصل - Fully Connected:\n",
        "  </font>\n",
        "  </div>\n",
        "\n",
        "![FC](https://drive.google.com/uc?export=view&id=15YcnB-3E2D-pGlssP3JPWrwd1_92xfcW)"
      ],
      "metadata": {
        "id": "Cq14deDU49XT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "  <div dir=rtl id=\"dataset\">\n",
        "  <font face=\"XB Zar\" size=4>\n",
        "      <font color=\"red\" size=5>\n",
        "      آشنایی با لایه کانولوشنی\n",
        "      </font>\n",
        "      <hr/>\n",
        "      همانطور که در اسلایدهای کلاس هم دیدید، تفاوت زیادی بین یادگیری یک مدل کاملا متصل و یک مدل پیچشی وجود دارد.\n",
        "      <br>\n",
        "      در مدل‌های Convolutional، ما با مدل‌هایی با پارامترهای بسیار کمتر، نتایجی بسیار بهتر روی دادگان تصویری می‌گیریم.\n",
        "      <br><br>\n",
        "      آزمایش انجام شده در اسلایدهای کلاس را در این قسمت می‌توانید خودتان انجام داده و نتایج را مقایسه کنید.\n",
        "      </hr>\n",
        "      \n",
        "  </font>\n",
        "  </div>\n"
      ],
      "metadata": {
        "id": "fay0-AqvwD5a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download the CIFAR10 dataset\n",
        "(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n",
        "\n",
        "# Preprocessing the data\n",
        "trainY = to_categorical(y_train)\n",
        "testY = to_categorical(y_test)\n",
        "\n",
        "x_train = x_train.astype('float32')\n",
        "x_test = x_test.astype('float32')\n",
        "x_train = x_train / 255.0\n",
        "x_test = x_test / 255.0\n",
        "\n",
        "# Inputs for the Dense Model\n",
        "dense_train = x_train.reshape(-1, 32*32*3)\n",
        "dense_test = x_test.reshape(-1, 32*32*3)"
      ],
      "metadata": {
        "id": "C9NMKTcixxgk"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "  <div dir=rtl id=\"dataset\">\n",
        "  <font face=\"XB Zar\" size=4>\n",
        "      <hr/>\n",
        "      مدلی کاملا متصل ساخته و آن را روی دیتای CIFAR10 آموزش می‌دهیم.\n",
        "      </hr>\n",
        "      \n",
        "  </font>\n",
        "  </div>\n"
      ],
      "metadata": {
        "id": "iqmIYOuKyMiI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# example of loading the cifar10 dataset\n",
        "from matplotlib import pyplot\n",
        "from keras.datasets import cifar10\n",
        "# load dataset\n",
        "# (trainX, trainy), (testX, testy) = cifar10.load_data()\n",
        "# summarize loaded dataset\n",
        "print('Train: X=%s, y=%s' % (x_train.shape, y_train.shape))\n",
        "print('Test: X=%s, y=%s' % (x_test.shape, y_test.shape))\n",
        "# plot first few images\n",
        "fig , ax = plt.subplots(3, 3)\n",
        "# plot first few images\n",
        "for i in range(9):\n",
        "  # plot raw pixel data\n",
        "  ax[i//3, i%3]. imshow (x_train[i])\n",
        "# show the figure\n",
        "fig.tight_layout()\n",
        "fig.set_figheight(8)\n",
        "fig.set_figwidth(8)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 578
        },
        "id": "LrdT01_p3eDu",
        "outputId": "5cd58db9-7670-4bd9-bd2d-e9c174a04bdc"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train: X=(50000, 32, 32, 3), y=(50000, 1)\n",
            "Test: X=(10000, 32, 32, 3), y=(10000, 1)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "-1RuGS8x2pxu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 991
        },
        "outputId": "666e35a8-1d5a-434b-8099-fb718449621f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Dense_Model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Dense_Layer_1 (Dense)       (None, 2048)              6293504   \n",
            "                                                                 \n",
            " Dense_Layer_2 (Dense)       (None, 1024)              2098176   \n",
            "                                                                 \n",
            " Dense_Layer_3 (Dense)       (None, 512)               524800    \n",
            "                                                                 \n",
            " Dense_Layer_4 (Dense)       (None, 128)               65664     \n",
            "                                                                 \n",
            " Softmax_Output_Layer (Dense  (None, 10)               1290      \n",
            " )                                                               \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,983,434\n",
            "Trainable params: 8,983,434\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Flatten, Dense\n",
        "\n",
        "dense_model = Sequential(\n",
        "    [\n",
        "        Dense(2048, input_dim=32*32*3, activation='relu', name='Dense_Layer_1'),\n",
        "        Dense(1024, activation='relu', name='Dense_Layer_2'),\n",
        "        Dense(512, activation='relu', name='Dense_Layer_3'),\n",
        "        Dense(128, activation='relu', name='Dense_Layer_4'),\n",
        "        Dense(10, activation='softmax', name='Softmax_Output_Layer'),\n",
        "    ],\n",
        "    name='Dense_Model'\n",
        ")\n",
        "\n",
        "\n",
        "dense_model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "dense_model.summary()\n",
        "plot_model(dense_model, show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  dense_history = dense_model.fit(dense_train, trainY, batch_size=128, epochs=10, verbose=1, validation_split=0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CB3qTRyvvO5l",
        "outputId": "1e59f40b-4875-4edc-b888-98d43a126dc6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "274/274 [==============================] - 5s 8ms/step - loss: 2.0343 - accuracy: 0.2549 - val_loss: 1.8709 - val_accuracy: 0.3275\n",
            "Epoch 2/10\n",
            "274/274 [==============================] - 2s 8ms/step - loss: 1.7589 - accuracy: 0.3616 - val_loss: 1.7097 - val_accuracy: 0.3854\n",
            "Epoch 3/10\n",
            "274/274 [==============================] - 2s 6ms/step - loss: 1.6674 - accuracy: 0.4023 - val_loss: 1.7009 - val_accuracy: 0.3878\n",
            "Epoch 4/10\n",
            "274/274 [==============================] - 2s 6ms/step - loss: 1.6091 - accuracy: 0.4235 - val_loss: 1.6153 - val_accuracy: 0.4222\n",
            "Epoch 5/10\n",
            "274/274 [==============================] - 2s 7ms/step - loss: 1.5534 - accuracy: 0.4434 - val_loss: 1.5622 - val_accuracy: 0.4403\n",
            "Epoch 6/10\n",
            "274/274 [==============================] - 2s 9ms/step - loss: 1.5128 - accuracy: 0.4580 - val_loss: 1.5374 - val_accuracy: 0.4515\n",
            "Epoch 7/10\n",
            "274/274 [==============================] - 3s 10ms/step - loss: 1.4703 - accuracy: 0.4721 - val_loss: 1.5351 - val_accuracy: 0.4487\n",
            "Epoch 8/10\n",
            "274/274 [==============================] - 3s 11ms/step - loss: 1.4294 - accuracy: 0.4893 - val_loss: 1.4810 - val_accuracy: 0.4733\n",
            "Epoch 9/10\n",
            "274/274 [==============================] - 3s 10ms/step - loss: 1.3944 - accuracy: 0.5011 - val_loss: 1.4673 - val_accuracy: 0.4819\n",
            "Epoch 10/10\n",
            "274/274 [==============================] - 4s 13ms/step - loss: 1.3595 - accuracy: 0.5127 - val_loss: 1.4837 - val_accuracy: 0.4704\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dense_test_accuracy = dense_model.evaluate(dense_test, testY)[1]\n",
        "print(\"Test Accuracy\",np.round((dense_test_accuracy)*100,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfjaG0Mhv8_J",
        "outputId": "fc572e95-5927-467f-829c-4dbccb395d5c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 1.4630 - accuracy: 0.4798\n",
            "Test Accuracy 47.98\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot our results: Accuracy\n",
        "\n",
        "plt.plot(dense_history.history['accuracy'])\n",
        "plt.plot(dense_history.history['val_accuracy'])\n",
        "plt.title('Dense Model Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pTh0nqb2zT3c",
        "outputId": "b7d288de-55da-4eec-ba6e-7fde0c75fd00"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot our results: Loss\n",
        "\n",
        "plt.plot(dense_history.history['loss'])\n",
        "plt.plot(dense_history.history['val_loss'])\n",
        "plt.title('Dense Model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "waamXlirzate",
        "outputId": "bd75b880-4cad-4ce8-919e-fc35c9579793"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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x3+tg+1pgkKf+vqdMHhBH/4RwFq89xLwxXtu3rZRSnaZ3FneRiDX/UO6RCr7Kr7C7HKWUOmsaBGfgqrFphAX5s1jnH1JK9QK+EwSFufD8PKg/+/7oqJBArhydyptbCjle2+iG4pRSyj6+EwTNDbB/NbzzK7ecbsGkLBqanbyy4YhbzqeUUnbxnSDIOBcu+jV89TJ89epZn25wciQT+sXy/LpDOJw6/5BSqufynSAAuOCXkD4R3v45HD941qe7cVIW+cfrWLOr9OxrU0opm/hWEPgHwLwnrMdLF4Kj+axOd+mwJJKigrXTWCnVo/lWEADEZMLM++DIevjkL2d1qkB/P743IYOPdpdx8GitmwpUSqnu5XtBADDiahj5Xfjov+HwurM61fcnZBDgJzyv8w8ppXoo3wwCgBn/C9EZsOTHUF95xqdJjAph+vBkXt1whLpGnX9IKdXz+G4QhETBvKegqgDe/sVZnWrBpCyq6ptZnlvgpuKUUqr7+G4QAKSPhym/ga2vwpb/O+PTjM+KYUhyJM+t1aUslVI9j28HAcAFP4eM86yrgmMHzugUIsKCSVnkFVWx8dBxNxeolFKepUHg528NKRU/WPpjcDSd0Wnmjk4hMiRAh5IqpXocDQKA6HSYdT/kfwkf/c8ZnSIsKICrx6bx7rYiSqvr3VygUkp5jgZBi+HzIOc6696CQ5+f0SlumJhJk8Pw8hc6/5BSqufQIGjt8v+G6EzrruO6rq810D8hggsGxvPP9Ydpdjg9UKBSSrmfBkFrwZFw1dNQXQRv3QVnMAJowaQsiqvq+XBHiQcKVEop99MgOFXaWLj4t7B9KWx5qcuHXzIkkdToUJ5be9DtpSmllCdoELRl8p2QeT68/Uso39elQ/39hOsnZrJu/zF2l1R7qECllHIfDYK2+PnDvMfBPxCW/KjLQ0rnj08nKMCP53UoqVKqB/BYEIjIMyJSKiLbTrO9j4i8KSJbRGS7iNzkqVrOSJ80mPUAFG6CNf/VpUNjw4OYNTKFpZvyqa4/s/sSlFKqu3jyiuBZYHo7228FdhhjRgFTgL+KSJAH6+m6YXNh9A3wyd/gwCddOnTBpExqGx0s3aTzDymlvJvHgsAY8zFwrL1dgEgRESDCte/ZrRTjCdPvhdj+sOwncKK9l3OyUenRjErrw+K1B3X+IaWUV7Ozj+AhIBsoBLYCi4wxbQ6+F5GFIrJBRDaUlZV1Z40QHAFXPw01pfDWnV0aUrpgUhb7ymr5fF+5BwtUSqmzY2cQXAbkAilADvCQiES1taMx5gljzDhjzLiEhITurNGSMhou+R3sWA6bn+/0YVeM7EtseBCL1x70WGlKKXW27AyCm4ClxrIXOAAMsbGe9p13B/S7EN79NRzd26lDQgL9uXZcOh/uKKGwos7DBSql1JmxMwgOA1MBRCQJGAzst7Ge9vn5wZWPQ0AwLLkZmhs7ddh152ZggH+uP+zZ+pRS6gx5cvjoS8BaYLCI5IvIzSJyi4jc4trlP4DzRGQrsBL4tTHmqKfqcYuoFJj9dyjKhdV/6tQh6bFhTB2SxEtfHKahWZeyVEp5nwBPndgY870OthcCl3rq73tM9iwY+wP47EE4Zyr0v6jDQxZMymRFXgnvbi1m7uhUz9eolFJdoHcWn4nL/gxxAzo9pPT8AfH0iw/XTmOllFfSIDgTQeHWkNLao/DG7R0OKfXzE26YmMmmwxVsK6jspiKVUqpzNAjOVN9RMO0e2PkWbHy2w92vGptGaKC/XhUopbyOBsHZmHgr9L8Y3vsNlO1ud9c+oYHMHZ3K8txCKk50bsSRUkp1Bw2Cs+HnB3MfhcBQ15DShnZ3XzApk4ZmJ69s0KUslVLeQ4PgbEX1hTkPQ/FXsPKP7e6a3TeKCVmxPPvZQQr0BjOllJfQIHCHITNg3M2w9iHYt6rdXf9l+mCq65uZ9fdP+Xyvd982oZTyDRoE7nLpnyB+MCz7qTWa6DTGZcWy/LbJxIUHcf3T63ny4/06O6lSylYaBO4SFGYNKa07Bstva3dIaf+ECJbdOpnpw5P5z3fyuP2lzZxo9L4ZuJVSvkGDwJ2SR8C0P8Dud2HD0+3uGhEcwMPfH8Ovpw/hna1FXPnw5xw8WttNhSql1Dc0CNzt3FusqSfe/1co3dnuriLCT6ecw3M/nEBJdT2zH/qU1TtLu6lQpZSyaBC4W8uQ0qAIa0hpU32Hh1wwMIE3bzuf9Ngwfvjclzy4cg9Op/YbKKW6hwaBJ0QmwdxHoGQbrPxDpw5Jjw1jyU/P48qcVP724W4WPr+RKl34XinVDTQIPGXQZTBhIax7BPas6NQhIYH+/PXaUfx+1lDW7Cpl7kOfsaek2sOFKqV8nQaBJ33nj5A4FF7/KdR0bq1lEeEHk/vxzx9PpKq+mTkPf8Y7W4s8XKhSypdpEHhSYChc9RTUV8Lyn3Vp4fsJ/WJ56/bzGZwcyc9e3MS97+7Eof0GSikP0CDwtKRhcOl/wJ4P4Isnu3Rocp8QXl44ke+fm8FjH+3jB//4guO1OmGdUsq9NAi6w4SFMPBS+OB3ULKjS4cGB/jz5ytH8N9XjWD9/mPMeuhTXdNAKeVWnQoCEVkkIlFieVpENolIz1tm0i4iMOcRCIlyDSnt+oRz88dn8Ootk3A4DVc9+jlLN+V7oFCllC/q7BXBD40xVVhrDMcANwD3eqyq3igiAeY+BqU7YPmtsG0J7F8DRV9BZUGn7jcYlR7Nm7efz+iMaH7+yhZ+/8Z2mhxOz9eulOrVOrt4vbj+OwN43hizXUSk3QNEngFmAqXGmOFtbP8VcF2rOrKBBGNMx4sA91QDp8HkO+Gz+60gOFVgGITFQVgshMZ+8zgszvoJjSE+LI4Xrojl4fX1PPL5bnYUVvHQdaNJjAzp/tejlOoVpDMzX4rIP4BUoB8wCvAH1hhjxrZzzIVADbC4rSA4Zd9ZwF3GmEs6qmXcuHFmw4YNHdbs1WrK4MRRa+H7E+XWRHUnyl2/tzxu9Xz96fsETphgqiSSqLgkwvoktAqN1kESe/JzQWHd+GKVUt5ARDYaY8a1ta2zVwQ3AznAfmPMCRGJBW5q7wBjzMciktXJ838PeKmT+/Z8EQnWT2c5mqHueBuhUU5dWRGbtu8lpKyCEY7jxFccQU6UQ33F6c8XEGqFQ+Z5cPn/WI+VUj6rs0EwCcg1xtSKyPXAGOABdxQgImHAdOC2dvZZCCwEyMjIcMef7Vn8A04bHnHAedMbWfRyLh/tLmP+uHT+MGcYIX7GCoNWofFNiJRDTSlsWwqH1sLVz0DGud3/upRSXqGzTUNfYTUJjQSeBZ4CrjXGXNTBcVnAW+01DYnIfOB6Y8yszhTcK5qGPMDhNNz34W4eWr2XUWl9ePT6saREh7Z/UMEmeO0mqDgCl/zO6r/w0xHFSvVG7TUNdfZffbOxEmMO8JAx5mEg0k31fRdfahbyEH8/4ZeXDebxG8ayr6yWWX//lLX7yts/KHUM/ORjGDrbmhzvxas6PRWGUqr36GwQVIvIb7CGjb4tIn5A4Nn+cRHpA1wELD/bcynLZcOSef3WyUSHBXL90+t56pMOlsIM6QNX/wNm3g+HPofHJsOBj7uvYKWU7TobBPOBBqz7CYqBNOB/2ztARF4C1gKDRSRfRG4WkVtE5JZWu10JfGCM0aW53GhAYgSv3zqZadmJ/OntPBa9nNv+UpgiMO4m+NFKKxiemw2r/wucju4rWillm071EQCISBIw3vXrF8YYW5bS0j6CzjPG8Miaffzlg10MTork8RvGkhkX3v5BDTXwzq9gyz8h83y46kmISumegpVSHnPWfQQici3wBXANcC2wXkSudl+JyhNEhFsvHsCzN02gqLKeWX//lDW7Osjv4Ai48lHrLujCzfDY+bDnw+4pWClli842Df0rMN4Yc6MxZgEwAfg3z5Wl3OmiQdZSmKkxYdz07Jc8tKoTS2HmfA8WroHIvvDi1fDhv4NDV0xTqjfqbBD4ndIUVN6FY5UXyIgLY+lPz2P2qBT+8sFubnlhI9UdLYWZMAh+tALG/RA+ewD+cTlUHO6egpVS3aazH+bvicj7IvIDEfkB8DbwjufKUp4QGuTP/fNz+PeZQ1m5s5RL7/uYF9cforG5nYnrAkNh5n1wzbNQtstqKsp7q9tqVkp5Xlc6i68CJrt+/cQYs8xjVbVDO4vdY+OhY/zp7Tw2H64gNTqU2y4ZwNVj0wj0b+e7wbED1g1ohZthwk+sBXcCgruvaKXUGWuvs7jTQeAtNAjcxxjDR7vLuG/FHrYcqSA9NpTbLx7IlWNSTx8IzY2w4vew7mHoO8q6ByHunG6tWynVdWccBCJSDbS1gwDGGBPlnhI7T4PA/YwxrN5Vyn0f7mFrQSWZcWHcfslA5uakEHC6QNj5Drz+U+teg1n3wwgdRKaUN9MrAtUpxhhW5pVy34rdbC+sol98OLdfMoA5Oan4+7Wx/ETFEWvFtSPrYcwCmP7fOsW1Ul5Kg0B1iTGGD3aUcP+KPeQVVdE/PpxF0wYyc2TKtwPB0QSr/wyf3gcJQ6xO5cQhttStlDo9DQJ1RpxOwwc7irl/xR52FlczIDGCO6YOZOaIvvidGgh7V8Kyn1h3Jl/xF8i5zpq6QinlFTQI1FlxOg3vbivmgZW72V1Sw6CkCBZNHcTlw5NPDoTqYlj6Y2vSupHz4Yq/QrC7JqlVSp0NDQLlFk6n4e2tRTywcg97S2sYkhzJndMGcunQVoHgdMAnf4U1/wWx/a1RRX1H2lu4UkqDQLmXw2l466tCHli5h/1ltWT3jXIFQhLS0hx08FNY8iNrdbTL/hPG/0ibipSykQaB8giH0/DGlgIeWLGHg+UnGJ4axZ1TBzE1O9EKhNqjsOwW2PshZM+G2X+H0Gi7y1bKJ2kQKI9qdjh5PbeQB1fu4fCxE4xM68Od0wZy8eBExBhY+5C1AlpUClz9LKSNtbtkpXyOBoHqFk0OJ8s2FfDgqj3kH69jVHo0d00byEWDEpD8DfDaD6G6EKb9HibequsjK9WNNAhUt2pyOFmyMZ+/r9pLQUUdozOiuWvaIC5IC0DeuA12vgUDL4O5j0J4nN3lKuUTNAiULRqbnby68QgPr9pLYWU94zJjuGvaQM47thT54HcQFg9XPw2Z59ldqlK9ngaBslVDs4NXNuTz8Kq9FFfVM6FfLP86polRaxfB8YPWTKbn3QZ90uwuValeS4NAeYX6Jgf/9+URHlmzl5KqBqZkhfA/ES+TuG+JNbR0+NUw+Q5IGmZ3qUr1OrYEgYg8A8wESo0xw0+zzxTgfiAQOGqMuaij82oQ9Hz1TQ5e+uIwj6zZR1l1A7MymvlN7Gr67nsFaaqFAdNg8iLIukDvPVDKTewKgguBGmBxW0EgItHA58B0Y8xhEUk8ZTnMNmkQ9B71TQ5eWHeIJz7eT2l1Axek+XNP33Wcs/8FpLYM+uZYgZA9G/wD7C5XqR7NtqYhEckC3jpNEPwMSDHG/K4r59Qg6H3qmxy8tjGfR9fso6CijlHJIfxHv62MOLQYObYPYrJg0m3WRHY6zbVSZ8Rbg6ClSWgYEAk8YIxZfJrzLAQWAmRkZIw9dOiQp0pWNmpyOFmeW8gjq/ey/2gtA+ND+OOQI5xb9AJ+BV9CaCxMWGj96LBTpbrEW4PgIWAcMBUIBdYCVxhjdrd3Tr0i6P0cTsO724p4aNVedhZXkx4Twr+NrGLqsZfx3/MeBITC6Otg0q3WxHZKqQ61FwR23tqZD7xvjKk1xhwFPgZG2ViP8hL+fsLMkSm8u+gCnlowjriIEBZ+FMR5h37Ma5OW0jxsHmx8Dv4+Fl79ARRssrtkpXo0O4NgOXC+iASISBhwLpBnYz3Ky4gI04Ymsexn5/HCzefSLz6cX66u59xtV/KPCW/ScO5tsHcVPHkxPDsT9nwIPWw4tFLewJOjhl4CpgDxQAlwD1afAMaYx1z7/Aq4CXACTxlj7u/ovNo05Ns2HDzGQ6v3smZXGVEhASycEM8Pwz4hbOPj1jxGicPgvNth+FUQEGR3uUp5Db2hTPU6W/MreXj1Xt7bXkxYkD8LJqTws/hcojY+AmV5EJUKE38GY2/UVdKUQoNA9WK7S6p5ZPVe3thSSO6WN9gAABQVSURBVIC/H98dl8YdmQeJ3/I4HPwEgvvA+B/CubdAZLLd5SplGw0C1esdPFrLYx/tY8mmfIyBeWNSWZRdQ+r2JyDvDfALsNZRPu8OSBhkd7lKdTsNAuUzCivqeOLj/bz0xWGaHE5mjkzhzjH+9N/zLOS+CM31MHiGdcdyxkS7y1Wq22gQKJ9TVt3AU5/u54W1h6htdHDp0CQWTYphWP7/wRdPQt0xSJtgBcLgGbpIjur1NAiUz6o40cg/PjvIPz47QFV9MxcOSuCO81MYd/wdawnNikMQN8CawiJ7tt6xrHotDQLl86rrm3hh3WGe/nQ/R2samZAVy21Tsrig6XPk8wegaIu1Y0w/SBsHqWMhdRz0HQkBwfYWr5QbaBAo5VLX6ODlLw/zxMf7KaqsZ2RaH26bcg7TIg/jd2QtFGyA/I3WPQkAfoGQPMIVDuOs/8b21+mxVY+jQaDUKRqaHSzdVMCja/Zx+NgJBidFcv3EDGaM6EtcRDBUFUL+hm+CoXAzNNVaB4fGfHPF0HL1EBZr7wtSqgMaBEqdRrPDyVtfFfHYR/vYWVyNv59wwcB45uak8p2hSYQHu9ZBcDRD2U5XMGyAgo1Qmge4/v3E9m8VDOOsqwi9s1l5EQ0CpTphZ3EVy3MLeSO3kIKKOkIC/fjO0GTmjErhwkEJBAWcMrKoodq6UmgJhvwNUFNsbfMPguSRrZqUxlr9D9qkpGyiQaBUFzidhk2Hj7M8t5C3txZxrLaRPqGBzBjRlzk5KUzIisXPr40PdGOgquDkJqWiXGg6YW0Pi2vVpDTWehwa070vTvksDQKlzlCTw8mne4/yRm4h728v5kSjg+SoEGbnpDB7VArDUqKQ9r7lO5qtuY9ah0PZTr5uUoobcHJfQ9JwbVJSHqFBoJQbnGhsZmVeKctzC/lodylNDsM5CeHMyUll9qgUsuLDO3ei+iqrSaklGAo2QE2JtU38ITDUmhLDP9AateQf2OpxgNXs1PJ8y37+Qacc03q/ANd/g1o9Ps25W/YLj7eCSZuyeg0NAqXc7HhtI+9uK2Z5bgHrDxwDYFR6NHNGpTBzVF8SI0M6fzJjoDLfCoTibdY0GI4mcDSCs8m6qnC6fv/6cRM4m13PtTxuOcb1uGW/lsfO5q69yOQRcP5dMHQu+Pl37VjldTQIlPKgwoo63vqqkOW5hWwvrMJP4Lxz4pmdk8L04clEhQTaXaLFmM6HR8k2+OxBKN9jdXJPvgNGfR8CuxBwyqtoECjVTfaWVvNGbiHLtxRyqPwEQQF+TB2SyJycFKYMTiQksAd9s3Y6Ydfb8MnfoHAThCfCpJ/BuB9CSB+7q1NdpEGgVDczxrAlv5LluQW8uaWIozUNRAYHMH14MnNyUpl0Thz+bY088kbGWGs7fHof7FsFwVEw/mY496cQmWR3daqTNAiUslGzw8m6/cdYnlvAe9uKqW5oJj4imFmj+jInJ5VRaX3aH3nkTQpz4bP7Ycdyq2N59HXW0qCx/e2uTHVAg0ApL1Hf5GD1Tmvk0apdpTQ2O8mMC2POqBRm56QyIDHC7hI7p3wffP4g5P7T6l8YdiVMvtOapE95JQ0CpbxQZV0T728v5o3cQj7fdxSngWEpUVw5OpU5OakkRPaAWU+ri2HdI/DlM9BYDedMtUYaZZ2vQ0+9jAaBUl6utKqet74q4vXcAr7Kr8TfT7hoUALzxqQyLTvJ+zuZ6ypgw9Ow7lGoLbNukjv/Ll30x4vYEgQi8gwwEyg1xgxvY/sUYDlwwPXUUmPMHzs6rwaB6u32llazdFMByzYXUFRZT2RIADNH9mXemDTGZcZ4d39CU521JOhnD1qL/sQPspqMRlyjd0zbzK4guBCoARa3EwS/NMbM7Mp5NQiUr3A4Dev2l7NkUz7vbbOmt8iIDWPemFTmjU4jIy7M7hJPz9EMO16HT++Hkq0QlWqtAjdmAQT3kH6QXsa2piERyQLe0iBQ6uzUNjTz/vZilm4q4LN9RzEGxmfFMG9MGjNG9KVPqJfctHYqY2DvSmvo6aFPrUn2JvwEJizUZUG7mTcHwRIgHyjECoXtpznPQmAhQEZGxthDhw55qGKlvF9RZR3LNhewZGM++8pqCQrw4ztDk7hqTCoXDkwgwN9L2+SPfGFdIex6GwLDYMyNMOlWiE63uzKf4K1BEAU4jTE1IjIDeMAYM7Cjc+oVgVIWYwxbCypZuqmA5bkFHD/RRHxEEHNyUpk3JpWhfTuYGdUupTvhswdg6yvW7yOuhcmLIHGIvXX1cl4ZBG3sexAYZ4w52t5+GgRKfVtjs5M1u0pZuqmAlTtLaHIYhiRHMm9MKnNzUkmM8sI5giqOwNqHYdNz1poNg2dYI43SJ9hdWa/klUEgIslAiTHGiMgE4DUg03RQkAaBUu07XtvIW1uLWLopn82HK/ATuGCgNRT10qHJhAZ52VDU2nL44gn44nGoOw6Zk61AGDBN70VoUVcBx/ZDaPQZ38Vt16ihl4ApQDxQAtwDBAIYYx4TkduAnwLNQB3wc2PM5x2dV4NAqc7bX1bz9VDUgoo6IoIDmDEimavGpDH+dCut2aWhBjYthrUPWSu9JY2wRhnF9oc+adZPbx5xdOIYHDtgfeAf2+f6737rLu46a6pzJi+C73Q4yr5NekOZUj7O6TSsP3CMpZvyeWdrEbWNDtJiQpk3OpUrx6TRr7OL6nSH5kbY+qrVj3B018nbQmNcoZDu+kn75vfodGuGVG+9gc0Y14f9/rY/7OsrWu0s1uuK7Qex51hhGNvfmsIjOuOM/rwGgVLqa3WNDj7YUcxrG/P5bK81tcWYjGjmjUlj1sgU+oR5yVDUljWgKwug8ojrJ9/6qXA9bqg8+Ri/QOiT+u2giHb9HpUKQR68/8IYOFFufbC39YFf37pesepq+ZBv/YEfk+X2tR80CJRSbSqurGd5bgFLNuWzu6SGIH8/pg1N5MrRaVw0KIGgAC/9dt2ivvKbcGgJiopWgVFdCMZ58jFhca1CwnUl0frKIjyh/b4JY6xpNFq+yZ/0gX8AGqq+2Vf8rHPG9oe4c07+wI/JhIDum09Kg0Ap1S5jDNsLq74eilpe20h0WCBXjLCmyh6XGeNd/Qmd5WiC6qJWVxKHTw6OiiPQVHvyMf7B325yam5o9YF/wJpgr4X4W801Ld/m41p9s4/O9JqpNTQIlFKd1uRw8smeMl7fXMiHO0qoa3KQGh3K7JwU5uakMjg50u4S3ccYq23+pCuJU/5bXWx9s4/JPLn5puUDPzoD/L2kOa0dGgRKqTNS29DMBzuKWZ5byCd7juJwWvcnzB2dyuxRKaREh9pdouc1N1pNRT3gw749GgRKqbN2tKaBt11TZW8+bI1wmdAvlrk5qcwYkUx0mHc0gai2aRAopdzqUHkty3MLeT23gP1ltQT6C1MGJzI3J5Wp2Ynev36CD9IgUEp5REsn8+ubC3hjSyGl1Q1EBAcwfXgyc3NSmXROHP49sZO5F9IgUEp5XMv6Ca9vLuC9bcVUNzSTEBnMrJEpzB2dwojUPt45CZ6P0CBQSnWr+iYHq3aW8vrmAtbsKqPR4aR/fDhzclKZk5NCljfdyewjNAiUUrapPNHEu9usTub1B45hDOSkRzMnJ4WZI1NIiOy+m6p8mQaBUsorFFbU8eaWQl7PLSSvqAp/P2HygHjm5qRw6bBkIoID7C6x19IgUEp5nd0l1by+uYDluYUUVNQREujHd4YmMzcnhQsHJRDorSut9VAaBEopr+V0GjYdPs7ruQW8/VURx080ERMWyIwRfZk9KoWxmTHeu/xmD6JBoJTqERqbXdNb5Bby4Y5i6pucRIcFcsngRKZmJ3HhoHgiQ3r2Hb52aS8ItEFOKeU1ggL8mJqdxNTsJGoamvl4dxkrdpSwalcpSzcXEOgvTOwfx7TsJKZmJ5IW48EppX2IXhEopbxes8PJpsMVrMgrYUVeCfvLrBlDhyRH8p2hVnCMTO3TM2dI7SbaNKSU6lX2l9WwMq+UD/NK2HDwGE4DCZHBTB2SyLTsJCYPiPe+tZltpkGglOq1jtc2smZ3KSvySvloVxk1Dc2EBPpx/oB4pmUncUl2IomR7l3tqyfSIFBK+YTGZidfHDj2dRNS/vE6AEalRzNtSCLThiYxJDnSJ6e6sCUIROQZYCZQaowZ3s5+44G1wHeNMa91dF4NAqVUZxhj2FVSbTUh7Sgh94g1dXZqdCjTsq1RSOf2jyU4wDeakOwKgguBGmDx6YJARPyBD4F64BkNAqWUp5RW17N6Zykf7ijl071l1Dc5iQgO4MJBVhPSxYMTiQnvvWsq2DJ81BjzsYhkdbDb7cASYLyn6lBKKYDEyBDmj89g/vgM6pscfLb3KCvySlmZV8I7W4vxExiXGcu0odbVwjkJEXaX3G082kfgCoK32roiEJFU4J/AxcAzrv3avCIQkYXAQoCMjIyxhw4d8lTJSikf43QathVWsmJHCSvyStlRVAVA//hwpmZbo5B6w93NtnUWdxAErwJ/NcasE5FnaScIWtOmIaWUJxVU1LEqr4QP80pZt6+cRod1d/PFgxOZmp3IhYMSiOqBdzd7axAcAFq67uOBE8BCY8zr7Z1Tg0Ap1V1qGpr5ZHcZK/JKWbWzhOMnmgj0F87tF/f11UJ6bM+4u9krg+CU/Z5FrwiUUl7M4TRsPnycD/NKWJlXyt7SGgAGJ0V+3a+QkxbttXc32zVq6CVgCta3/RLgHiAQwBjz2Cn7PosGgVKqBzl4tPbr+xW+PHgch9MQHxHMJUMSmJadxPkD4wkL8p7p3PSGMqWU8qDKE01f3928Zlcp1fXNBAX4MfmcOKYNTWLqkCSS+9h7d7MGgVJKdZMmh5MvDxzjQ9fVwpFj1t3NI1L7fN2vMCwlqtvvbtYgUEopGxhj2FNaYzUh7Shh85EKjIG+fUKY6rq7eVL/OEICPX93swaBUkp5gaM1Dazaad3E9vHuo9Q1OQgL8ueCgfFMzU7ikiGJxEcEe+RvaxAopZSXqW9ysHZ/OSvzSlixo5TiqnpEYHR6NNOGJjEtO4mBiRFua0LSIFBKKS9mjGF7YdXXo5C2FVh3N2fEhjE1O5HvZCcxvl8sgWdxd7MGgVJK9SBFlXWsdM2D9Nm+chqbnUSGBHDHJQP58YX9z+icumaxUkr1IH37hHL9xEyun5jJicZmPtlzlJV5JR4bgqpBoJRSXiwsKIDLhiVz2bBkj/2Nnj2dnlJKqbOmQaCUUj5Og0AppXycBoFSSvk4DQKllPJxGgRKKeXjNAiUUsrHaRAopZSP63FTTIhIGXDoDA+PB466sZyeTt+Pk+n78Q19L07WG96PTGNMQlsbelwQnA0R2XC6uTZ8kb4fJ9P34xv6Xpyst78f2jSklFI+ToNAKaV8nK8FwRN2F+Bl9P04mb4f39D34mS9+v3wqT4CpZRS3+ZrVwRKKaVOoUGglFI+zmeCQESmi8guEdkrInfbXY+dRCRdRFaLyA4R2S4ii+yuyW4i4i8im0XkLbtrsZuIRIvIayKyU0TyRGSS3TXZRUTucv0b2SYiL4mIZ5YIs5lPBIGI+AMPA5cDQ4HvichQe6uyVTPwC2PMUGAicKuPvx8Ai4A8u4vwEg8A7xljhgCj8NH3RURSgTuAccaY4YA/8F17q/IMnwgCYAKw1xiz3xjTCLwMzLG5JtsYY4qMMZtcj6ux/qGn2luVfUQkDbgCeMruWuwmIn2AC4GnAYwxjcaYCnurslUAECoiAUAYUGhzPR7hK0GQChxp9Xs+PvzB15qIZAGjgfX2VmKr+4F/AZx2F+IF+gFlwD9cTWVPiUi43UXZwRhTAPwFOAwUAZXGmA/srcozfCUIVBtEJAJYAtxpjKmyux47iMhMoNQYs9HuWrxEADAGeNQYMxqoBXyyT01EYrBaDvoBKUC4iFxvb1We4StBUACkt/o9zfWczxKRQKwQeNEYs9Tuemw0GZgtIgexmgwvEZEX7C3JVvlAvjGm5QrxNaxg8EXTgAPGmDJjTBOwFDjP5po8wleC4EtgoIj0E5EgrA6fN2yuyTYiIlhtwHnGmL/ZXY+djDG/McakGWOysP5/scoY0yu/9XWGMaYYOCIig11PTQV22FiSnQ4DE0UkzPVvZiq9tOM8wO4CuoMxpllEbgPex+r5f8YYs93msuw0GbgB2Coiua7nfmuMecfGmpT3uB140fWlaT9wk8312MIYs15EXgM2YY2020wvnWpCp5hQSikf5ytNQ0oppU5Dg0AppXycBoFSSvk4DQKllPJxGgRKKeXjNAiU6kYiMkVnOFXeRoNAKaV8nAaBUm0QketF5AsRyRWRx13rFdSIyH2u+elXikiCa98cEVknIl+JyDLXHDWIyAARWSEiW0Rkk4ic4zp9RKv5/l903bWqlG00CJQ6hYhkA/OBycaYHMABXAeEAxuMMcOAj4B7XIcsBn5tjBkJbG31/IvAw8aYUVhz1BS5nh8N3Im1NkZ/rDu9lbKNT0wxoVQXTQXGAl+6vqyHAqVY01T/n2ufF4Clrvn7o40xH7mefw54VUQigVRjzDIAY0w9gOt8Xxhj8l2/5wJZwKeef1lKtU2DQKlvE+A5Y8xvTnpS5N9O2e9M52dpaPXYgf47VDbTpiGlvm0lcLWIJAKISKyIZGL9e7natc/3gU+NMZXAcRG5wPX8DcBHrpXf8kVkruscwSIS1q2vQqlO0m8iSp3CGLNDRH4HfCAifkATcCvWIi0TXNtKsfoRAG4EHnN90LeerfMG4HER+aPrHNd048tQqtN09lGlOklEaowxEXbXoZS7adOQUkr5OL0iUEopH6dXBEop5eM0CJRSysdpECillI/TIFBKKR+nQaCUUj7u/wEdxJ1zK3LjxgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  <div dir=rtl id=\"dataset\">\n",
        "  <font face=\"XB Zar\" size=4>\n",
        "      <font color=\"red\" size=5>\n",
        "      آشنایی با لایه کانولوشنی\n",
        "      </font>\n",
        "      <hr/>\n",
        "      حال، مدلی Convolutional طراحی کرده و تصاویر CIFAR10 را روی آن آموزش می‌دهیم.\n",
        "      </hr>\n",
        "      \n",
        "  </font>\n",
        "  </div>\n"
      ],
      "metadata": {
        "id": "BU5gSLrhzmZb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CNN architecture\n",
        "Convolutional_model = Sequential(\n",
        "    [\n",
        "        Conv2D(32, (3, 3), input_shape=(32, 32, 3),activation='relu', name='Conv_Layer_1'),\n",
        "        MaxPool2D(pool_size=(2, 2), name='Max_Pool_1'),\n",
        "        Conv2D(64, (3, 3),activation='relu', name='Conv_Layer_2'),\n",
        "        Conv2D(128, (3, 3),activation='relu', name='Conv_Layer_3'),\n",
        "        Flatten(name='Flatten'),\n",
        "        Dense(128, activation='relu', name='Dense_Flat_1'),\n",
        "        Dense(10, activation='softmax', name='Softmax_Output_Layer')\n",
        "    ],\n",
        "    name='Convolutional_Model'\n",
        ")\n",
        "\n",
        "Convolutional_model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "\n",
        "Convolutional_model.summary()\n",
        "plot_model(Convolutional_model, show_shapes=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "TePtKRZ2zidO",
        "outputId": "707bfbbd-eddd-4d04-83d2-45fec7eaa8a9"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"Convolutional_Model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " Conv_Layer_1 (Conv2D)       (None, 30, 30, 32)        896       \n",
            "                                                                 \n",
            " Max_Pool_1 (MaxPooling2D)   (None, 15, 15, 32)        0         \n",
            "                                                                 \n",
            " Conv_Layer_2 (Conv2D)       (None, 13, 13, 64)        18496     \n",
            "                                                                 \n",
            " Conv_Layer_3 (Conv2D)       (None, 11, 11, 128)       73856     \n",
            "                                                                 \n",
            " Flatten (Flatten)           (None, 15488)             0         \n",
            "                                                                 \n",
            " Dense_Flat_1 (Dense)        (None, 128)               1982592   \n",
            "                                                                 \n",
            " Softmax_Output_Layer (Dense  (None, 10)               1290      \n",
            " )                                                               \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,077,130\n",
            "Trainable params: 2,077,130\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with tf.device('/device:GPU:0'):\n",
        "  conv_history = Convolutional_model.fit(x_train, trainY, batch_size=128, epochs=10, verbose=1, validation_split=0.3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P7jOnf0U0IO9",
        "outputId": "7b723ba4-cade-4735-83c6-b2f916b0a28d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "274/274 [==============================] - 12s 13ms/step - loss: 1.5714 - accuracy: 0.4290 - val_loss: 1.3038 - val_accuracy: 0.5398\n",
            "Epoch 2/10\n",
            "274/274 [==============================] - 3s 11ms/step - loss: 1.1907 - accuracy: 0.5764 - val_loss: 1.2918 - val_accuracy: 0.5618\n",
            "Epoch 3/10\n",
            "274/274 [==============================] - 3s 10ms/step - loss: 1.0212 - accuracy: 0.6411 - val_loss: 1.0404 - val_accuracy: 0.6334\n",
            "Epoch 4/10\n",
            "274/274 [==============================] - 3s 11ms/step - loss: 0.8812 - accuracy: 0.6897 - val_loss: 0.9541 - val_accuracy: 0.6710\n",
            "Epoch 5/10\n",
            "274/274 [==============================] - 3s 11ms/step - loss: 0.7914 - accuracy: 0.7231 - val_loss: 0.9199 - val_accuracy: 0.6857\n",
            "Epoch 6/10\n",
            "274/274 [==============================] - 3s 10ms/step - loss: 0.6794 - accuracy: 0.7635 - val_loss: 0.8965 - val_accuracy: 0.6967\n",
            "Epoch 7/10\n",
            "274/274 [==============================] - 3s 13ms/step - loss: 0.5872 - accuracy: 0.7949 - val_loss: 0.9221 - val_accuracy: 0.6998\n",
            "Epoch 8/10\n",
            "274/274 [==============================] - 3s 11ms/step - loss: 0.4965 - accuracy: 0.8267 - val_loss: 0.9193 - val_accuracy: 0.7045\n",
            "Epoch 9/10\n",
            "274/274 [==============================] - 3s 10ms/step - loss: 0.4025 - accuracy: 0.8618 - val_loss: 0.9880 - val_accuracy: 0.6975\n",
            "Epoch 10/10\n",
            "274/274 [==============================] - 3s 10ms/step - loss: 0.3155 - accuracy: 0.8922 - val_loss: 1.1302 - val_accuracy: 0.6859\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "conv_test_accuracy = Convolutional_model.evaluate(x_test, testY)[1]\n",
        "print(\"Test Accuracy\",np.round((conv_test_accuracy)*100,2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aEqPsy_g0JHt",
        "outputId": "5c0ac8c3-82af-4498-fdc2-bec4c76d6849"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 1.1774 - accuracy: 0.6735\n",
            "Test Accuracy 67.35\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot our results: Accuracy\n",
        "\n",
        "plt.plot(conv_history.history['accuracy'])\n",
        "plt.plot(conv_history.history['val_accuracy'])\n",
        "plt.title('Conv Model Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "mzuaDmfa0LXJ",
        "outputId": "ec300c79-87dc-41e1-87fe-9b9d1822b06b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's plot our results: Loss\n",
        "\n",
        "plt.plot(conv_history.history['loss'])\n",
        "plt.plot(conv_history.history['val_loss'])\n",
        "plt.title('Conv Model Loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pOtWWQtp0Nmf",
        "outputId": "ec2e0391-339a-468d-ef15-8cdba06f4396"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  <div dir=rtl id=\"dataset\">\n",
        "  <font face=\"XB Zar\" size=4>\n",
        "      <hr/>\n",
        "      نتایج دو مدل را با هم مقایسه می‌کنیم.\n",
        "      <br>\n",
        "      همانطور که مشخص است مدل Convolutional با وجود داشتن پارامترهای خیلی کمتر، با اختلاف زیادی بهتر عمل می‌کند.\n",
        "      </hr>\n",
        "      \n",
        "  </font>\n",
        "  </div>\n"
      ],
      "metadata": {
        "id": "36I1o2BC0eQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(dense_history.history['val_accuracy'])\n",
        "plt.plot(conv_history.history['val_accuracy'])\n",
        "plt.title('Dense vs. Conv Model Accuracy')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['Dense', 'Conv'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "6eFgQIB-0QKh",
        "outputId": "602dd1cb-da97-47e6-b272-1a8555c5fa60"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  <div dir=rtl id=\"dataset\">\n",
        "  <font face=\"XB Zar\" size=4>\n",
        "      <hr/>\n",
        "      معماری مدل‌ها و Hyperparameter ها را به دلخواه تغییر دهید و نتایج مختلف را آزمایش کنید.\n",
        "      <br>\n",
        "\n",
        "\n",
        "\n",
        "می‌توانید سعی کنید با راهکارهایی که در فصل قبل آموختید، جلوی Overfit شدن مدل‌ها را گرفته و نتایج را دوباره مقایسه کنید.\n",
        "\n",
        "\n",
        "  </font>\n",
        "  </div>\n"
      ],
      "metadata": {
        "id": "dEOYWf_C02w1"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TBu8JwYw0bGj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}